You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
84 lines
2.7 KiB
84 lines
2.7 KiB
# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved. |
|
# |
|
# Licensed under the Apache License, Version 2.0 (the "License"); |
|
# you may not use this file except in compliance with the License. |
|
# You may obtain a copy of the License at |
|
# |
|
# http://www.apache.org/licenses/LICENSE-2.0 |
|
# |
|
# Unless required by applicable law or agreed to in writing, software |
|
# distributed under the License is distributed on an "AS IS" BASIS, |
|
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. |
|
# See the License for the specific language governing permissions and |
|
# limitations under the License. |
|
|
|
from __future__ import absolute_import |
|
from __future__ import division |
|
from __future__ import print_function |
|
|
|
from paddle.utils import try_import |
|
|
|
from paddlers.models.ppdet.core.workspace import register, serializable |
|
from paddlers.models.ppdet.utils.logger import setup_logger |
|
logger = setup_logger(__name__) |
|
|
|
|
|
@register |
|
@serializable |
|
class QAT(object): |
|
def __init__(self, quant_config, print_model): |
|
super(QAT, self).__init__() |
|
self.quant_config = quant_config |
|
self.print_model = print_model |
|
|
|
def __call__(self, model): |
|
paddleslim = try_import('paddleslim') |
|
self.quanter = paddleslim.dygraph.quant.QAT(config=self.quant_config) |
|
if self.print_model: |
|
logger.info("Model before quant:") |
|
logger.info(model) |
|
|
|
self.quanter.quantize(model) |
|
|
|
if self.print_model: |
|
logger.info("Quantized model:") |
|
logger.info(model) |
|
|
|
return model |
|
|
|
def save_quantized_model(self, layer, path, input_spec=None, **config): |
|
self.quanter.save_quantized_model( |
|
model=layer, path=path, input_spec=input_spec, **config) |
|
|
|
|
|
@register |
|
@serializable |
|
class PTQ(object): |
|
def __init__(self, |
|
ptq_config, |
|
quant_batch_num=10, |
|
output_dir='output_inference', |
|
fuse=True, |
|
fuse_list=None): |
|
super(PTQ, self).__init__() |
|
self.ptq_config = ptq_config |
|
self.quant_batch_num = quant_batch_num |
|
self.output_dir = output_dir |
|
self.fuse = fuse |
|
self.fuse_list = fuse_list |
|
|
|
def __call__(self, model): |
|
paddleslim = try_import('paddleslim') |
|
self.ptq = paddleslim.PTQ(**self.ptq_config) |
|
model.eval() |
|
quant_model = self.ptq.quantize( |
|
model, fuse=self.fuse, fuse_list=self.fuse_list) |
|
|
|
return quant_model |
|
|
|
def save_quantized_model(self, |
|
quant_model, |
|
quantize_model_path, |
|
input_spec=None): |
|
self.ptq.save_quantized_model(quant_model, quantize_model_path, |
|
input_spec)
|
|
|